Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.

Computer science graduates often find software engineering knowledge and skills are more in demand after they join the industry. However, given the lecture-based curriculum present in academia, it is not an easy undertaking to deliver industry-standard knowledge and skills in a software engineering classroom as such lectures hardly engage or convince students. Overcoming Challenges in Software Engineering Education: Delivering Non-Technical Knowledge and Skills combines recent advances and best practices to improve the curriculum of software engineering education. This book is an essential reference source for researchers and educators seeking to bridge the gap between industry expectations and what academia can provide in software engineering education.

This book provides state-of-the-art and interdisciplinary topics on solving matrix eigenvalue problems, particularly by using recent petascale and upcoming post-petascale supercomputers. It gathers selected topics presented at the International Workshops on Eigenvalue Problems: Algorithms; Software and Applications, in Petascale Computing (EPASA2014 and EPASA2015), which brought together leading researchers working on the numerical solution of matrix eigenvalue problems to discuss and exchange ideas - and in so doing helped to create a community for researchers in eigenvalue problems. The topics presented in the book, including novel numerical algorithms, high-performance implementation techniques, software developments and sample applications, will contribute to various fields that involve solving large-scale eigenvalue problems.

This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.

Have you ever seen a promising project get abandoned right when it seemed like it was getting ready to launch, or a project that always seemed like it was going to launch "next month," but took many months or even years before it actually launched? Scrum may be the solution to such woes. It is a project management methodology that, when applied faithfully, will increase the rate at which your project is completed, the enjoyment your team has while working on it, and its commercial success. Converting to scrum can be a challenge for any team, but it is particularly painful for part time, virtual, and student teams. Successful Scrumbutt helps teams quickly learn scrum project management and adapt it to their unique situations. Author Noah Dyer illustrates key techniques for maintaining a team's productivity and enthusiasm for a project across its lifetime in fun, engaging, and slightly irreverent ways. Key Features Learn key techniques for maintaining enthusiasm for your project across its lifetime and have the confidence that every hour spent on your project is having maximum benefit. Includes instructions for using Assembla and other popular and free Scrum software. This will save people time identifying and learning a software package to help implement Scrum principles. Activities to introduce people to Scrum and practice Scrum principles. These are fun ways to learn Scrum in a low pressure atmosphere where delivery and execution are not paramount. Soft Skills Sections help readers understand not only the action to be taken and the principle, but how to effectively communicate with others regarding Scrum practices, particularly problem solving. Excel templates, activities for implementing Scrum in a low tech environment and planning cards that make Scrum planning activities easier and feel more official are included for download and easy printing. Do It Now: Tips for integrating the principle in a chapter right away, even if full Scrum is not yet fully understood and achievable.

Have you ever seen a promising project get abandoned right when it seemed like it was getting ready to launch, or a project that always seemed like it was going to launch "next month," but took many months or even years before it actually launched? Scrum may be the solution to such woes. It is a project management methodology that, when applied faithfully, will increase the rate at which your project is completed, the enjoyment your team has while working on it, and its commercial success. Converting to scrum can be a challenge for any team, but it is particularly painful for part time, virtual, and student teams. Successful Scrumbutt helps teams quickly learn scrum project management and adapt it to their unique situations. Author Noah Dyer illustrates key techniques for maintaining a team's productivity and enthusiasm for a project across its lifetime in fun, engaging, and slightly irreverent ways. Key Features Learn key techniques for maintaining enthusiasm for your project across its lifetime and have the confidence that every hour spent on your project is having maximum benefit. Includes instructions for using Assembla and other popular and free Scrum software. This will save people time identifying and learning a software package to help implement Scrum principles. Activities to introduce people to Scrum and practice Scrum principles. These are fun ways to learn Scrum in a low pressure atmosphere where delivery and execution are not paramount. Soft Skills Sections help readers understand not only the action to be taken and the principle, but how to effectively communicate with others regarding Scrum practices, particularly problem solving. Excel templates, activities for implementing Scrum in a low tech environment and planning cards that make Scrum planning activities easier and feel more official are included for download and easy printing. Do It Now: Tips for integrating the principle in a chapter right away, even if full Scrum is not yet fully understood and achievable.

Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.

Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine.This is the first book to explore GMDH using MATLAB (matrix laboratory) language. Readers will learn how to implement GMDH in MATLAB as a method of dealing with big data analytics. Error-free source codes in MATLAB have been included in supplementary material (accessible online) to assist users in their understanding in GMDH and to make it easy for users to further develop variations of GMDH algorithms.

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

Auf der Grundlage aktueller Entwicklungen der Wirtschaftsinformatik stellen die Autoren Konzepte dar, die als Richtlinie fA1/4r die kA1/4nftige Entwicklung von einem die operationalen Systeme, Data Warehouse-, Informations-, Experten- und Data Mining Systeme umfassenden Anwendungssystem dienen kAnnen.
Neben notwendigen theoretischen Aspekten wird basierend auf der umfangreichen praktischen Erfahrung der Autoren in der Anwendungsentwicklung fA1/4r Kreditinstitute ein konkreter Praxisbezug hergestellt. Die Autoren untermauern ihre Betrachtungen heutiger und kA1/4nftiger Anwendungsentwicklungen sowohl mit den theoretischen Grundlagen als auch mit dem praktischen Nutzen der Methoden. Mit konkretem Praxisbezug werden flexible operationale Systeme erlAutert und eine optimale Informationsversorgung der EntscheidungstrAger aufgezeigt. AussagefAhige Grafiken machen das Buch fA1/4r jeden Interessierten verstAndlich.

This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.

This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it, although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle.

Zoom into the new world of remote collaboration While a worldwide pandemic may have started the Zoom revolution, the convenience of remote meetings is here to stay. Zoom For Dummies takes you from creating meetings on the platform to running global webinars. Along the way you'll learn how to expand your remote collaboration options, record meetings for future review, and even make scheduling a meeting through your other apps a one-click process. Take in all the advice or zoom to the info you need - it's all there! Discover how to set up meetings Share screens and files Keep your meetings secure Add Zoom hardware to your office Get tips for using Zoom as a social tool Award-winning author Phil Simon takes you beyond setting up and sharing links for meetings to show how Zoom can transform your organization and the way you work.

The International Federation for Information Processing, IFIP, is a multinational federation of professional technical organisations concerned with information processing. IFIP is dedicated to improving communication and increased understanding among practitioners of all nations about the role information processing can play in all walks of life. This Working Conference, Secondary School Mathematics in the World of Communication Technologies: Learning, Teaching and the Curriculum, was organised by Working Group 3.1, Informatics in Secondary Education, ofiFIP Technical Committee for Education, TC3. This is the third conference on this theme organised by WG 3.1, the previous two were held in Varna, Bulgaria, 1977, and Sofia, Bulgaria, 1987-proceedings published by North-Holland Elsevier. The aim of the conference was to take a forward look at the issue of the relationships between mathematics and the new technologies of information and communication in the context of the increased availability of interactive and dynamic information processing tools. The main focus was on the mathematics education of students in the age range of about ll to 18 years and the following themes were addressed: * Curriculum: curriculum evolution; relationships with informatics; * Teachers: professional development; methodology and practice; * Learners: tools and techniques; concept development; research and theory; * Human and social issues: culture and policy; personal impact.

You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

This is today's most complete guide to regression analysis with Microsoft (R) Excel for any business analytics or research task. Drawing on 25 years of advanced statistical experience, Microsoft MVP Conrad Carlberg shows how to use Excel's regression-related worksheet functions to perform a wide spectrum of practical analyses. Carlberg clearly explains all the theory you'll need to avoid mistakes, understand what your regressions are really doing, and evaluate analyses performed by others. From simple correlations and t-tests through multiple analysis of covariance, Carlberg offers hands-on, step-by-step walkthroughs using meaningful examples. He discusses the consequences of using each option and argument, points out idiosyncrasies and controversies associated with Excel's regression functions, and shows how to use them reliably in fields ranging from medical research to financial analysis to operations. You don't need expensive software or a doctorate in statistics to work with regression analyses. Microsoft Excel has all the tools you need-and this book has all the knowledge! Understand what regression analysis can and can't do, and why Master regression-based functions built into all recent versions of Excel Work with correlation and simple regression Make the most of Excel's improved LINEST() function Plan and perform multiple regression Distinguish the assumptions that matter from the ones that don't Extend your analysis options by using regression instead of traditional analysis of variance Add covariates to your analysis to reduce bias and increase statistical power

Without question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students' anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses. Key features of the text: Step-by-step instruction and screenshots Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter Call-out boxes provided, highlighting important information as appropriate SPSS output explained, with written results provided using the popular, widely recognized APA format End-of-chapter exercises included, allowing for additional practice SPSS datasets available on the publisher's website New to the Fourth Edition: Fully updated to SPSS 28 Updated screenshots in full color to reflect changes in SPSS software system (version 28) Exercises updated with up-to-date examples Exact p-values provided (consist with APA recommendations)

Unlike other books about R, written from the perspective of statistics, R for Programmers: Mastering the Tools is written from the perspective of programmers, providing a channel for programmers with expertise in other programming languages to quickly understand R. The contents are divided into four sections: The first section consists of the basics of R, which explains the advantages of using R, the installation of different versions of R, and the 12 frequently used packages of R. This will help you understand the tool packages, time series packages, and performance monitoring packages of R quickly. The second section discusses the server of R, which examines the communication between R and other programming languages and the application of R as servers. This will help you integrate R with other programming languages and implement the server application of R. The third section discusses databases and big data, which covers the communication between R and various databases, as well as R's integration with Hadoop. This will help you integrate R with the underlying level of other databases and implement the processing of big data by R, based on Hadoop. The fourth section comprises the appendices, which introduce the installation of Java, various databases, and Hadoop. Because this is a reference book, there is no special sequence for reading all the chapters. You can choose the chapters in which you have an interest. If you are new to R, and you wish to master R comprehensively, simply follow the chapters in sequence.

Your team is stressed; priorities are unclear. You're not sure what your teammates are working on, and management isn't helping. If your team is struggling with any of these symptoms, these four case studies will guide you to project success. See how Kanban was used to significantly improve time to market and to create a shared focus across marketing, IT, and operations. Each case study comes with illustrations of the Kanban board and diagrams and graphs to help you see behind the scenes. Learn a Lean approach by seeing how Kanban made a difference in four real-world situations. You'll explore how four different teams used Kanban to make paradigm-changing improvements in software development. These teams were struggling with overwork, unclear priorities, and lack of direction. As you discover what worked for them, you'll understand how to make significant changes in real situations.The four case studies in this book explain how to: * Improve the full value chain by using Enterprise Kanban * Boost engagement, teamwork, and flow in change management and operations * Save a derailing project with Kanban * Help an office team outside IT keep up with growth using Kanban What seems easy in theory can become tangled in practice. Discover why "improving IT" can make you miss your biggest improvement opportunities, and why you should focus on fixing quality and front-end operations before IT. Discover how to keep long-term focus and improve across department borders while dealing with everyday challenges. Find out what happened when using Kanban to find better ways to do work in a well-established company, including running multi-team development without a project office. You'll inspire your team and engage management to make it easier to develop better products. What You Need: This is a case study book, so there are no software requirements. The book covers the relevant bits of theory before presenting the case studies.

Recent computer-based tools for project planning and management focus on user-friendliness and interconnectivity. However, these programs function on the Critical Path Method, or CPM, which was created in the 1950s. These programs, which involve simplistic models and methods, ignore the fact that the underlying computations on which they function have become woefully inadequate for the complex projects of today. The product of nearly a decade of work, The Dynamic Progress Method: Using Advanced Simulation to Improve Project Planning and Management provides an overview of the research conducted while illustrating some of the issues with current approaches. It presents the Dynamic Progress Method (DPM), an innovative simulation-based approach to project management. It also includes instructions on how to use the accompanying DPM-based simulation tool pmBLOX to plan, manage, and analyze projects. This groundbreaking book is a must-have resource for project planning and management. It introduces a new and better way of planning, estimating, and managing projects that corrects some of the fundamental flaws of the CPM. It brings the computational integrity of planning simulations up to speed with modern needs, making it useful not only to current project managers but also to students who will become project managers.

* Targests readers with a background in programming, interested in an introduction/refresher in statistical hypothesis testing * Uses Python throughout * Provides the reader with the opportunity of using the book whenever needed rather than following a sequential path.

Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing. The book covers the foundations of classical test theory (CTT), test reliability, validity, and scaling as well as item response theory (IRT) fundamentals and IRT for dichotomous and polytomous items. The authors explore the latest IRT extensions, such as IRT models with covariates, multidimensional IRT models, IRT models for hierarchical and longitudinal data, and latent class IRT models. They also describe estimation methods and diagnostics, including graphical diagnostic tools, parametric and nonparametric tests, and differential item functioning. Stata and R software codes are included for each method. To enhance comprehension, the book employs real datasets in the examples and illustrates the software outputs in detail. The datasets are available on the authors' web page.

With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a "boot-camp" on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors' website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment.

There is no shortage of incentives to study and reduce poverty in our societies. Poverty is studied in economics and political sciences, and population surveys are an important source of information about it. The design and analysis of such surveys is principally a statistical subject matter and the computer is essential for their data compilation and processing. Focusing on The European Union Statistics on Income and Living Conditions (EU-SILC), a program of annual national surveys which collect data related to poverty and social exclusion, Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R presents a set of statistical analyses pertinent to the general goals of EU-SILC. The contents of the volume are biased toward computing and statistics, with reduced attention to economics, political and other social sciences. The emphasis is on methods and procedures as opposed to results, because the data from annual surveys made available since publication and in the near future will degrade the novelty of the data used and the results derived in this volume. The aim of this volume is not to propose specific methods of analysis, but to open up the analytical agenda and address the aspects of the key definitions in the subject of poverty assessment that entail nontrivial elements of arbitrariness. The presented methods do not exhaust the range of analyses suitable for EU-SILC, but will stimulate the search for new methods and adaptation of established methods that cater to the identified purposes.